The “background” description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present invention.
Modeling solar energy production data is a complex task with numerous variables and inputs. To calculate energy production for a given system, specific latitude/longitude, tilt azimuth, shading, insolation, and soiling, among other inputs, unique to that system and home must be understood. Existing solar design software requires nontrivial wait times to view energy production information after updating characteristics of the solar module or system, including tilt, azimuth, module count, and product type.
With existing methods for creating proposal-stage designs, determining the energy output of a design change is time-consuming. With these existing methods, every change to system or module size, tilt, azimuth, or product type requires re-calculating energy production data, which is typically performed at a remote server. This means that each iteration requires a request from a user device to a server for re-calculation, goal-seeking for either energy or monetary savings, and a reply from the server to the user device. As can be appreciated, this process can be very time-consuming, taking minutes or hours to complete.
Simplistic solar design software can provide quick iteration based on generic factors—for example, by providing a slider that uses a generic solar output curve for a given region. This option may allow the user device to be self-contained and eliminate communication between the user device and a remote server that performs the intensive calculations, but this method does not consider home-specific characteristics, including potential solar module locations, azimuth, tilt, and other critical design inputs. Thus, this method often yields imprecise or inaccurate estimates that lead to incorrect energy and savings expectations for homeowners.